Instructions to use Hezam/ArabicT5-49GB-small-classification-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hezam/ArabicT5-49GB-small-classification-generation with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Hezam/ArabicT5-49GB-small-classification-generation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Hezam/ArabicT5-49GB-small-classification-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("Hezam/ArabicT5-49GB-small-classification-generation", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hezam/ArabicT5-49GB-small-classification-generation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hezam/ArabicT5-49GB-small-classification-generation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hezam/ArabicT5-49GB-small-classification-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Hezam/ArabicT5-49GB-small-classification-generation

SGLang

How to use Hezam/ArabicT5-49GB-small-classification-generation with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Hezam/ArabicT5-49GB-small-classification-generation" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hezam/ArabicT5-49GB-small-classification-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Hezam/ArabicT5-49GB-small-classification-generation" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hezam/ArabicT5-49GB-small-classification-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Hezam/ArabicT5-49GB-small-classification-generation with Docker Model Runner:
```
docker model run hf.co/Hezam/ArabicT5-49GB-small-classification-generation
```

Hezam commited on Dec 30, 2023

Commit

dc3180c

1 Parent(s): 0d11952

Update README.md

Browse files

Files changed (1) hide show

README.md +9 -5

README.md CHANGED Viewed

@@ -18,9 +18,11 @@ tags:
 widget:
 - example_title: الرياضة
 - text: |
-    عصام عبدالفتاح: الاتحاد لا يستحق ركلة جزاء أمام النصر
 ---
 # # ArabicT5 Model for Arabic News Classification and Generation
   - In this model focus on classifying and generating news Arabic.
@@ -69,7 +71,7 @@ model = T5ForConditionalGeneration.from_pretrained(model_name)
 tokenizer = T5Tokenizer.from_pretrained(model_name)
 generation_pipeline = pipeline("text2text-generation",model=model,tokenizer=tokenizer)
-text = " خسارة مدوية لليفربول امام تولوز وفوز كبير لبيتيس"
 output= generation_pipeline(text,
                     num_beams=10,
@@ -81,6 +83,8 @@ output= generation_pipeline(text,
 output
 ```
-category: 4 article: كتبت لبني عبد الله خسر فريق ليفربول بقياده البرتغالي جوسفالدو فيريرا نظيره تولوز بهدف نظيف ضمن منافسات الجوله ال عمر مسابقه الدوري الانجليزي الممتاز بهدفين مقابل هدف المباراه جمعتهما اليوم الاحد استاد
-```bash
-category: 4 article: كتبت لبني عبد الله خسر فريق ليفربول بقياده البرتغالي جوسفالدو فيريرا نظيره تولوز بهدف نظيف ضمن منافسات الجوله ال عمر مسابقه الدوري الانجليزي الممتاز بهدفين مقابل هدف المباراه جمعتهما اليوم الاحد استاد```

 widget:
 - example_title: الرياضة
 - text: |
+    أوقفوا القتل الجماعي في غزة
 ---
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/645817bb72b60ae7a37f8f40/F2ViP8tPRmMLSMejtQqOP.jpeg)
 # # ArabicT5 Model for Arabic News Classification and Generation
   - In this model focus on classifying and generating news Arabic.
 tokenizer = T5Tokenizer.from_pretrained(model_name)
 generation_pipeline = pipeline("text2text-generation",model=model,tokenizer=tokenizer)
+text = "أوقفوا القتل الجماعي في غزة"
 output= generation_pipeline(text,
                     num_beams=10,
 output
 ```
+category: 1 article: كتب عبد اللطيف صبح قال الرءيس الفلسطيني محمود عباس في تصريح ل اليوم السابع وقفوا القتل الجماعي في مدينه غزة مءكدا يجب يوقفوا قتل المدنيين العزل في قطاعي غزة والضفه وغزه واوقفوا القتل الجماع
+```
+bash
+category: 1 article: كتب عبد اللطيف صبح قال الرءيس الفلسطيني محمود عباس في تصريح ل اليوم السابع وقفوا القتل الجماعي في مدينه غزة مءكدا يجب يوقفوا قتل المدنيين العزل في قطاعي غزة والضفه وغزه واوقفوا القتل الجماع
+```